فهرست مطالب hani rezayan

Quality of life studies in order to identify the weaknesses of deprived areas, increase the level of satisfaction among citizens, ranking places and its role as an effective tool in urban management and planning. The purpose of this study is to create a fuzzy model including effective criteria for evaluating the quality of life in urban areas in areas 3, 4, 5 and 6 of Mashhad. In this regard, first, the effective criteria in quality of life that were obtained from distance measurement data, statistics and spatial data in 5 areas; Economic (employment rate, unemployment, home ownership and rent), social (literacy rate, divorce and migration), physical (type of materials and type of housing structure, slope and population density), access to services (educational, medical centers, Cultural, recreational and communication network) and environmental (greenery, air pollution and surface temperature) were extracted and then weighed using the network analysis process. Then, for spatial modeling of quality of life, the selected criteria were combined using the gamma fuzzy model. The results indicate that the quality of life in the central areas of the study area has a better quality of life, but by moving away from the central areas to the outskirts of the study area, the quality of life gradually decreases. Therefore, in order to improve the quality of life index of the citizens of neighborhoods that are not in a good condition in terms of quality of life, it is necessary to make changes to improve the economic, social, physical, spatial and environmental dimensions. In the following study, Moran spatial autocorrelation index (0.57) emphasizes the non-random nature of the distribution of urban quality of life characteristics and the existence of a cluster pattern in the study area.

The advent of Web 2.0 enables the users to interact and prepare free unlimited real time data. This advantage leads us to exploit Volunteer Geographic Information (VGI) for real time crisis management. Traditional estimation methods for earthquake damages are expensive and time consuming. In contrast, volunteer and web-based service are near real time with almost no cost services. the lack of accessible real time data collection services causes delayed-emergency responses for disasters like an earthquake. This drawback is critical when we encounter a problem like buried people with valuable seconds for emergency rescue operation. The current research aims to design and implement a web-based volunteer data collection service for rapid estimation of earthquake damages and number of buried people.

To investigate the capacity of VGI in rapid estimation of earthquake, a technical frame work based on the web technologies has been programmed and implemented. The designed service is comprised of server and client sides.The client side is a two-side browser-based service includes volunteers (users) and managers pages. On the user page, volunteers have a web page to enter and fill in the blank forms and taking a photograph of the target building and compare it with pictures. They watch the sample pictures in different level of damages and compare their building with the samples and give a grade of the most similar picture with their building. This grading process leads the server to analysis and classify the incoming data and create the heatmaps for managers. On the managers page two online discrete heatmaps for the both earthquake damages and buried people are displayed. In fact, the heatmaps present the online and real time quantitative situation of the building damages and buried persons as hot spots. These hotspots have the first priority for giving emergency services. The manager page also exploits query tools to request different level of details and classes from the server side. The server side is responsible for receiving, saving, spatial analysis and transmission of the requested result to the client side. This task is carried out by the exchange side. As the citizens are entered to the browser-based service and fill in the blank forms for building damages based on the mentioned guideline and report the buried people, These forms are transmitted to the server side and a geo-server performs spatial analysis including Heatmap, distance and clustering analysis. Then, a real time damage and buried people map are prepared and delivered to the client side. The server updates the created maps whenever a new data is submitted. By this, a real time damage and buried people maps are accessible for official managers to conduct a goal-oriented emergency operation and a preliminary earth quake damages on city building blocks. After the technical frame work has been designed, it was tested in Oshanvieh city by 132 volunteers on the scene for an earthquake.

To investigate the capability of volunteer geographic information for earth quake afterwards, the designed service mentioned in the methodology was utilized on Oshnavieh city. It was assumed that an earthquake has occurred. 132 volunteers participated for the data collection process. According to the crisis management organization experts, 102 reports of the total 132 reports are correct that shows the accuracy of 76.52 percent. Besides the building damage level based on the defined guideline, the citizens also select their vital needs after the earthquake. the most requested vital needs are warm stuffs, medicine, water and foods respectively. Unfortunately, the participation rate is ranged from some seconds after the earthquake to three days. This means that some citizens have filled and transmitted their data three days after the earthquake. In the following, a comparison between the designed service and traditional earthquake damage estimation methods (in situ) was carried out. The result shows that field-based methods for a city like Oshnavieh need about 20 days. However, the designed volunteer-based service what is programmed and implemented in the current research does this job by 3 days.

As the results show, the proposed service designed in this research implements the damage estimation process 6.5 times faster than the governmental procedures. This proves the efficiency of the research achievements. Besides the velocity, traditional damage estimation methods are expensive compare to volunteer-based data collection and processing which are almost free, scalable and pervasive.

 Public health and sanitation as an important and vital issue has always been considered by academicians and urban planners and managers. It has led to adopt policies and actions by governments and related organizations. Though, creating healthy cities is a long-standing idea that has been around for decades, but in recent months, as the Covid 19 epidemic, which usually spreads faster in crowded cities, has plagued every country in the world, it has received more attention again. One of the concerns of city planners and managers in the current situation is how to protect the living environment and create resilient cities against such diseases. One of the approaches that has been proposed in order to enhance health in human habitats is healthy city idea. The purpose of this study is to analyze the status of healthy city indicators and the spatial distribution of these indicators in the new city of Sahand.

 The study area of this research is the new city of Sahand, in the northwest of Iran, at a distance of 20 km from the city of Tabriz. The city has an area of 498 ha and a population of 80795 people. In the research, 38 indicators were selected as indicators of a healthy city and were classified into 4 dimensions. Part of the data was extracted from the GIS file of the 2016 Iranian Population and Housing Public Census and the other part was gathered through a survey using a questionnaire. Sample people including 378 household heads. The data was entered into the GIS to form a uniform shapefile. Then, by producing spatial distribution maps, spatial analysis was performed. Then, by transferring the data to SPSS software, a one-sample t-test was performed.

 T-test results showed that the mean of the 38 indicators is 2.99, which is slightly lower than the assumed mean (3). Among the four dimensions of the indicators, the physical one shows a better situation the others, so that the figure is 3.69, which is 0.69 higher than the assumed mean. As the city has constructed in recent decades in accordance to a pre-designed plan, the indicators have shown a better situation in this regard. After the physical dimension, the environmental one shows a higher value than the assumed mean and it was 3.33.In terms of environmental dimesion, relatively large distance from pollution resources, being enclosed in a valley and northwest winds, has removed air pollution from the city. The low density of cars on the roads has caused the residents to be satisfied with the lack of noise pollution. Connecting almost all the houses to the municipal sewage network, proper slope of lands, low density of population and the absence of worn buildings are some of the environmental advantages of the city. At the same time, poor waste management and non-observance of environmental cleanliness by citizens are low-scoring indicators in this dimension.The values of socio-cultural and economic dimensions are lower than the assumed mean.  Their values are 2.63 and 2.52 respectively. High value of the indicators of literacy, safety and security are the three indicators with higher values in this dimension. Onthe contrary, medical facilities, cultural services, leisure and sports facilities are indicators with a lower value in this dimension. In the economic dimension, except for employment, insurance coverage and job satisfaction, the other indicators show a very low value.In terms of spatial distribution, the indicators of a healthy city in Phase 1 of the city has a better situation. In this Phase, the average valueof the indicators is 3.08. In Phase 2, in the southern parts, it has a higher value, but the in phase 2, it is slightly lower than the assumed mean (2.97). At most areas of the Phases of 3 and 4, where construction is still ongoing and service coverage, infrastructure and facilities are not completed, the value of  indicators are low. In Phase 3, the average value of the indicators is 2.95 and in phase 4 it is 2.76.

Analyzing healthy city indicators indicated that Sahand represents a medium situation of a complete healthy city. In this city, for reasons that are mostly originated from national economy, there are many shortcomings in the economic indicators of a healthy city. However, due to the existence of a proper labor market near the city, unemployment rate is lower and insurance coverage is higher than most cities in Iran. But due to inflation, the incomes of the urban community, most of which are salaried, have not been able to provide them with the goods and services they need to live healthily. Due to the government's financial inability in recent years, it has not been made sufficient investment in urban services, facilities and infrastructure.The dormitory function of the city and the low financial capacity of its resident could not provide the municipality with the necessary financial resources to invest in  developing urban facilities and infrastructures . In terms of spatial distribution, in the zones and phases where the construction was done earlier (Phases 1, 2, 3 and 4, respectively), indicators values were higher than new constructed ones. In areas under construction or semi-residential, market conditions lead to less private sector activity.

Examining the effects of climate change on the oak spatial distribution, as the main species of Zagros forests and its ecological and economic values is of significant importance. Here, we used species distribution models for simulating current climatic suitability of oak and its potential changes in 2050 and 2070. For this purpose, five regression-based and machine learning approaches, four climatic variables related to temperature and precipitation and two optimistic (RCP 2.6) and pessimistic (RCP 8.5)  greenhouse-gas scenarios were used. The results of measuring the accuracy of models by AUC indicated the good performance of all algorithms and Random Forest achieved the highest accuracy (AUC = 0.95) among other methods. The results showed that in both time periods and under both scenarios, changes will occur in oak spatial distribution and the most severe one would be a 42.9 percent loss in the oak climatic suitability in 2070 under pessimistic scenario (RCP 8.5).

Methodology

The present studyinvestigates the effect of gradual reduction of noise from data collected by sensors, accelerometers, magnetometers, gyroscopes, and GPS technology in smartphones on improvement of vehicle positioning. The proposed method is based on using acceleration, azimuth, latitude, longitude and roll angle parameters as an input for the Kalman algorithm and investigates the effect of reducing noise produced by these parameters using the least-squares method onimprovement of the resulting position calculated by the Kalman algorithm. To reach this aim, the roll angle parameter is extracted from the angular Velocity() in y-direction and the azimuth parameter is extracted from the magnetic field() in both x and y directions. These parameters along with the acceleration(a) parameter in x and y directions and the geographic coordinates are selected for the Kalman filtering algorithm. In the proposed method, data received from sensors share common sources of noise produceddue to drift, random movements and bias errors.To reduce this noise independently and systematically, method of averaging with the least-squares is usedfor data produced by each sensor. Thus, noise in the received data is considered as a random parameter and noise reduction is performed based on the percentage of changes in the corrected and observed data in the range of 1 to 10%. Kalman algorithm is implemented for 10 levels of noise reduction and the results areinvestigated and compared.The filter calculates and improves an estimate of position vector x, denoted by  with minimum mean square error using a recursive model. The main objective is to derive an accurate estimate of   for the state of the observed system at time of k. Implementing Kalman filter consists of a prediction step and an updating step. The result is compared todata received from a more accurate reference using RMSE.  

The study area consists of lane no. 2 of the South-North (East-West) Azadegan Highway, Tehran, Iran with a total area of about 26km. Results show that compared to the reference data, using Kalman filter has decreased errorsin positioning the car from 0.8274 m to 0.6763 m with a 2%noise reduction. With a 10% noise reduction, the accuracy of this method has increases to 0.6771 m. This improved accuracy is due to noise reduction and consequently an increase in the correlation between the parameters. Accordingly, the threshold limit for noise reduction and improved positioning using Kalman filter is low and can be recognized by an investigation of a few lowlimits. According to the findings, although reducing the effect of noise can improve positioning with Kalman filter and smart phone sensors, irregular changes in the accuracy of noise reduction methods require determining an optimal percentage for noise reduction.

Complexity of multi dimension developments and infrastructures intensifies the land related challenges to adopt legal laws, restrictions and responsibilities.  Although, multi dimension estates have been registering for many years, as the complexity of the estates are increased, disadvantages of the 2D cadaster is more appeared. Hence, the 3D cadaster has been a necessity for sustainable development. Visualizing is one of the important components of 3D cadaster. In the current research, for efficient and effective visualization of land ownerships and their related 3D information, firstly, the needed essentials of cadaster visualization systems have been classified into 3 groups including: cadaster, visualization and independent properties. Then, the trends of 3D visualization developments for older plugin and WebGL based technologies have been considered. Finally, a number of the most important systems according to the needed criteria for web-based 3D cadaster were evaluated and consequently, Cesium virtual environment has been selected as the best for the development purpose. To develop a system for 3D cadaster visualization, 2D building properties was converted to 3D using different software and then, land law properties were added and subsequently, Building Information Model (BIM) was provided. HTML5, JavaScript and CSS languages along with WebGL library and Cesium API were utilized. The implemented service is able to display WFS-based standard vector layers as well as WMS image of OGC standard. The system has the possibilities of 3D visualization like web-based 3D cadaster visualization and land law properties over the web. This enables the user to make the printed output of BIM along with descriptive information of the buildings.

Nowadays, due to the complexity of interior space of buildings, the need arises for indoor navigation inside such spaces. Indoor navigation systems may be helpful for emergency evacuation of the crowd in natural hazards such as earthquake as well as human-made disasters. These systems can also act as a decision support system for officials. Literature survey on indoor navigation services shows that a large number of researches have been conducted around designing and implementing such systems but automatic indoor spaces topology extraction of the current building information models remains as a challenge. This research aims to introduce, design and implement a web-based indoor navigation system using CityGML data model in LOD4 (level of detail) to overcome the mentioned problem. The architecture of the current research is a browser-based web application service such that the data model processing and graph creation is implemented on the server side, the client interface and calculated path are represented on the client side (browser). Through the CityGML data model processing, firstly, the building navigable spaces such as room floor, doors and stairs are extracted and then, each space as a node and the connections between the nodes are defined as edges, are imported to the navigation graph. Programming on the server side has been performed by Python language and web development languages including HTML (Hypertext Markup language), JavaScript, JQuery and AJAX are used on the client side. Cesium virtual globe has been exploited to display the data model and the calculated route. To evaluate the introduced methodology and designed service, a three floor house with CityGML format in LOD4 was used as the case study. Generally, a client can request a 3D calculated path by selecting the source and destination points on the client browser. The server receives the request and returns the response as a 3D line to the client browser on the Cesium environment. In addition, a descriptive graphical user interface for visual inception of the route is offered to the users on their browser. One of the advantages of the designed web application is that, the service is implemented on the browser. Hence, all devices equipped with a browser have possibility to run the 3D routing service. Besides the mentioned cross-platform capability, average expectation time of the graphical interface loading, data module processing and path finder module are 7.03 milliseconds, 12.42 seconds and 2.44 seconds respectively that visits a valuable criteria in emergency situations like an earthquake phenomenon. Regarding this fact that CityGML is a new data model and supported by a few software, the introduced architecture causes less implementation costs as well as automation of these systems.

Due to the complexity of air pollution action, artificial intelligence models specifically, neural networks are utilized to simulate air pollution. So far, numerous artificial neural network models have been used to estimate the concentration of atmospheric PMs. These models have had different accuracies that scholars are constantly exceed their efficiency using numerous parameters. The current research aims to compare Elman and Jordan recurrent networks for error distribution and validation to estimate atmospheric particular matters concentration in Ahvaz city. The used parameters are relative humidity, air pressure, temperature and aerosol optical depth. The latter one is extracted from MODIS sensor images and air pollution monitoring stations. The results show that Jordan model with RMSE of 219.9 milligram per cubic meter has more accuracy rather than Elman model with RMSE of 228.5. The value of R2 index that shows the linear relation between the estimated from the model and observed values for Jordan is equal to 0.5 that implies 50% estimation accuracy. The value is because of MODIS spatial resolution, inadequacy in numbers as well as spatial distribution of meteorological station inside the study area. According to the results of the current research, it seems that air pollution monitoring stations have to be increased in terms of numbers and suitable spatial distribution. Also, other ancillary data like volunteer geographic air pollution data entry using mobile connected cheap sensors as portable stations may be used to implement more accurate simulation for air pollution.

Accessibility to precise spatial and real time data plays a valuable role in the velocity and quality of flood relief operation and subsequently, scales the human and financial losses down. Flood real time data collection and processing, for instance, precise location and situation of flood victims may be a big challenge in Iran regarding the hardware facilities (such as high resolution aerial imagery devices) owned by the correspond organizations. To overcome the mentioned inabilities as well as reducing the financial costs for real time monitoring purpose of a flood, the current research intended to use the capacity of the flood victims and other volunteers to collect and upload real time data to rescue themselves. By means of this, flood real time spatial and non-spatial data collection is applicable via public and per-person participation based on the needs of each victims. The current Open Source workflow has been so designed that by using a browser like Mozilla, Explorer, Chrome and etc., and without the need for installing any software, the victim transmits his/her exact geographic location (captured automatically by the designed web service) and other multimedia data such as video-photo. Also, the flood-affected person announces the type of the damage and consequently, demanded rescue operation to the managers as a text information. After data processing on the server, the information is represented as a real time rescue map for decision making. The rescue plan may be mapped based on the singular aid as well as plural plan in the cluster form specialized for a particular group of victims in each bounding box. To design the web service, a client architecture for victims, other volunteers and managers has been developed, for implementing the service, Open Source technologies, server-side and client-side programming languages, Geoserver and WFS (Web Feature Service) standard adopted by OGC for spatially-enabled representation of victims demands, have been exploited. The research result is a browser-based service in which the client service offers automatic zooming to the current location of the clients and sends the rescue request including personal identifications and the type of injury using PHP (stands for Hypertext Preprocessor) and SQL (Structured Query Language). In the other side, on the client side designed for managers, the requested rescue submitted by the victims and other volunteers are mapped and displayed real time by OpenLayers and WFS. The result introduces an efficient applicable method for gathering real time and high accuracy geographic-multimedia-text data collection and consequently, extremely reduces the relief operation costs. Finally, the proposed methodology causes better performance and spatially clustering of victims to decrease the aftermath of the flood in a region like Iran suffers from the lack of expensive hardware technologies for precise data collection and transmission.

Air pollution has become one of the main problems of cities. Among the sources of air pollution, vehicular traffic plays an important role. Planning for efficient management and control of the air pollution caused by vehicular traffic requires accurate information on spatio-temporal dispersion of the pollutions. This research studies 3D spatio-temporal dispersion of NOx pollution caused by vehicular traffic at Valieasr-Fatemi intersection resides in Tehran, Iran. It is selected for being crowded and having the required meteorological and pollution data sensed by the Air Quality Control Corp. of Tehran Municipality.
This study uses GRAL that is a local micro-scale air dispersion model defined based on Euleran-Lagrangian dispersion models. It investigates the level of spatio-temporal autocorrelation generated by GRAL simulations at both 2D and 3D modes and discusses how it adapts with the reality.
Adopting the GRAL air pollution dispersion model, streets are defined as the linear source of pollution of NOx caused by vehicular traffic. The traffic rate is estimated based on street areas and directions, the designed average traffic velocity, traffic volume and car passage counting at the intersection. The 3D geometry of the buildings is also added to the model. All the required data that were available for winter of 2007 are gathered and introduced into the model.
The model is executed at 9 heights vary from 1.7 m to 52.5 m. These heights are defined covering a range from an average human level height to average building height and above. These levels are considered both separately in 2D mode and integrated into a 3D mode. The formation of NOx clusters is investigated analyzing their autocorrelation using Moran Index at global and local scale.
The calculated Moran-I at global scale at each 9 levels of heights, varies from 0.7 to 0.9 that depicts the validity of the GRAL model adopted to simulate the expected autocorrelation of pollution density affected by spatial issues. The Moran-I increases at higher levels as less air turbulence happens. However the result show that the turbulence increases temporarily at about 10m to 15m which are the average building heights. At local scale, the Moran-I/Anselin shows that HH clusters dominate at lower levels, around streets central areas that are farther from the buildings, and around the intersections. At higher levels, esp. higher than buildings average height, the LL clusters dominate. However the HH clusters formed around intersections, while are shrank, are still visible at high levels. The turbulence caused by building fronts and their down wash effect is also shown in the result as no definite cluster is formed near the buildings front and back.
The autocorrelation analysis is also carried for an integrated 3D model consists of all the 9 levels of heights. Considering the weight matrix for a 20m 2D neighborhood and 1m/s dispersion of the pollution vertically, the global calculated Moran-I equals 0.229 which shows existence of a spatio-temporal autocorrelation of the results generated by GRAL. At local scale the results show that the HH clusters have higher temporal dispersion rate than LL clusters.